48,982 research outputs found
Parameter Sensitivity Analysis of Social Spider Algorithm
Social Spider Algorithm (SSA) is a recently proposed general-purpose
real-parameter metaheuristic designed to solve global numerical optimization
problems. This work systematically benchmarks SSA on a suite of 11 functions
with different control parameters. We conduct parameter sensitivity analysis of
SSA using advanced non-parametric statistical tests to generate statistically
significant conclusion on the best performing parameter settings. The
conclusion can be adopted in future work to reduce the effort in parameter
tuning. In addition, we perform a success rate test to reveal the impact of the
control parameters on the convergence speed of the algorithm
Motion Planning of Uncertain Ordinary Differential Equation Systems
This work presents a novel motion planning framework, rooted in nonlinear programming theory, that treats uncertain fully and under-actuated dynamical systems described by ordinary differential equations. Uncertainty in multibody dynamical systems comes from various sources, such as: system parameters, initial conditions, sensor and actuator noise, and external forcing. Treatment of uncertainty in design is of paramount practical importance because all real-life systems are affected by it, and poor robustness and suboptimal performance result if it’s not accounted for in a given design. In this work uncertainties are modeled using Generalized Polynomial Chaos and are solved quantitatively using a least-square collocation method. The computational efficiency of this approach enables the inclusion of uncertainty statistics in the nonlinear programming optimization process. As such, the proposed framework allows the user to pose, and answer, new design questions related to uncertain dynamical systems.
Specifically, the new framework is explained in the context of forward, inverse, and hybrid dynamics formulations. The forward dynamics formulation, applicable to both fully and under-actuated systems, prescribes deterministic actuator inputs which yield uncertain state trajectories. The inverse dynamics formulation is the dual to the forward dynamic, and is only applicable to fully-actuated systems; deterministic state trajectories are prescribed and yield uncertain actuator inputs. The inverse dynamics formulation is more computationally efficient as it requires only algebraic evaluations and completely avoids numerical integration. Finally, the hybrid dynamics formulation is applicable to under-actuated systems where it leverages the benefits of inverse dynamics for actuated joints and forward dynamics for unactuated joints; it prescribes actuated state and unactuated input trajectories which yield uncertain unactuated states and actuated inputs.
The benefits of the ability to quantify uncertainty when planning the motion of multibody dynamic systems are illustrated through several case-studies. The resulting designs determine optimal motion plans—subject to deterministic and statistical constraints—for all possible systems within the probability space
Review of Metaheuristics and Generalized Evolutionary Walk Algorithm
Metaheuristic algorithms are often nature-inspired, and they are becoming
very powerful in solving global optimization problems. More than a dozen of
major metaheuristic algorithms have been developed over the last three decades,
and there exist even more variants and hybrid of metaheuristics. This paper
intends to provide an overview of nature-inspired metaheuristic algorithms,
from a brief history to their applications. We try to analyze the main
components of these algorithms and how and why they works. Then, we intend to
provide a unified view of metaheuristics by proposing a generalized
evolutionary walk algorithm (GEWA). Finally, we discuss some of the important
open questions.Comment: 14 page
Determination of multifractal dimensions of complex networks by means of the sandbox algorithm
Complex networks have attracted much attention in diverse areas of science
and technology. Multifractal analysis (MFA) is a useful way to systematically
describe the spatial heterogeneity of both theoretical and experimental fractal
patterns. In this paper, we employ the sandbox (SB) algorithm proposed by
T\'{e}l et al. (Physica A, 159 (1989) 155-166), for MFA of complex networks.
First we compare the SB algorithm with two existing algorithms of MFA for
complex networks: the compact-box-burning (CBB) algorithm proposed by Furuya
and Yakubo (Phys. Rev. E, 84 (2011) 036118), and the improved box-counting (BC)
algorithm proposed by Li et al. (J. Stat. Mech.: Theor. Exp., 2014 (2014)
P02020) by calculating the mass exponents tau(q) of some deterministic model
networks. We make a detailed comparison between the numerical and theoretical
results of these model networks. The comparison results show that the SB
algorithm is the most effective and feasible algorithm to calculate the mass
exponents tau(q) and to explore the multifractal behavior of complex networks.
Then we apply the SB algorithm to study the multifractal property of some
classic model networks, such as scale-free networks, small-world networks, and
random networks. Our results show that multifractality exists in scale-free
networks, that of small-world networks is not obvious, and it almost does not
exist in random networks.Comment: 17 pages, 2 table, 10 figure
Supervised Quantum Learning without Measurements
We propose a quantum machine learning algorithm for efficiently solving a
class of problems encoded in quantum controlled unitary operations. The central
physical mechanism of the protocol is the iteration of a quantum time-delayed
equation that introduces feedback in the dynamics and eliminates the necessity
of intermediate measurements. The performance of the quantum algorithm is
analyzed by comparing the results obtained in numerical simulations with the
outcome of classical machine learning methods for the same problem. The use of
time-delayed equations enhances the toolbox of the field of quantum machine
learning, which may enable unprecedented applications in quantum technologies
Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems
Development of robust dynamical systems and networks such as autonomous
aircraft systems capable of accomplishing complex missions faces challenges due
to the dynamically evolving uncertainties coming from model uncertainties,
necessity to operate in a hostile cluttered urban environment, and the
distributed and dynamic nature of the communication and computation resources.
Model-based robust design is difficult because of the complexity of the hybrid
dynamic models including continuous vehicle dynamics, the discrete models of
computations and communications, and the size of the problem. We will overview
recent advances in methodology and tools to model, analyze, and design robust
autonomous aerospace systems operating in uncertain environment, with stress on
efficient uncertainty quantification and robust design using the case studies
of the mission including model-based target tracking and search, and trajectory
planning in uncertain urban environment. To show that the methodology is
generally applicable to uncertain dynamical systems, we will also show examples
of application of the new methods to efficient uncertainty quantification of
energy usage in buildings, and stability assessment of interconnected power
networks
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